Using a dataframe's name as string

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The entry point to programming Spark with the Dataset and DataFrame API,,append:Only the new rows in the streaming DataFrame/Dataset will be written to the sink, append:Only the new rows in the streaming DataFrame/Dataset will be written to the sink ,Maps each group of the current DataFrame using a pandas udf and returns the result as a DataFrame

Example_snippet/controller/utility/_string.js/ >>> spark = . .
>>> spark = SparkSession.builder\
   ....appName("Word Count")\
   ....config("spark.some.config.option", "some-value")\
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The name of the column in the returned DataFrame is the same as the original column,,The name of column to search in the projection list of this DataFrame

Example_snippet/controller/utility/_string.js/ >>> with ConnectionContext('ad. . .
>>> with ConnectionContext('address', port, 'user', 'password') as cc:
   ...df = (cc.table('MY_TABLE', schema = 'MY_SCHEMA')
      ....filter('COL3 > 5')'COL1', 'COL2'))
   ...pandas_df = df.collect()
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So to convert all the City names in our column to uppercase, we can add ,str followed by the upper method as shown:,Now the City column of our original ufo dataframe contains City names that are uppercase

Example_snippet/controller/utility/_string.js/ # bracket notationufo[‘City’] . . .
# bracket notationufo[‘City’] # dot notationufo.City
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You can name the dataframe with the following, and then call the name wherever you like:, 2 I need to have the name as a variable, import pandas as pd df = pd

Example_snippet/controller/utility/_string.js/ import pandas as pd df = pd.Da. . .
import pandas as pd
df = pd.DataFrame(data = np.ones([4, 4])) = 'Ones'

print >>>
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Trying to store a dataframe into a new one with the name of a variable, Not sure how to code it exactly

Example_snippet/controller/utility/_string.js/ df <- data.frame(iris) x <- "F. . .
df < -data.frame(iris)
x < -"Flowers"
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Whether to print index (row) labels,,Write out the column names

Example_snippet/controller/utility/_using.js/ >>> d = {'col1': [1, 2, 3], 'c. . .
>>> d = {
      'col1': [1, 2, 3],
      'col2': [4, 5, 6]
   } >>>
   df = pd.DataFrame(d) >>>
col1 col2
0 1 4
1 2 5
2 3 6